• Journal of the Korean Institute of Rural Architecture
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• v.10 no.1
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• pp.83-92
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• 2008

This study aims to examine the perceptual features of street buildings, which affect the urban scape, and establish design guidelines by types of buildings. According to this purpose, this study conducted document research, field survey through site visits and questionnaire survey for each subject. The field survey was carried out to study the exterior form characteristics of street buildings, and images of the streetscape in Gyeongju. For the questionnaire survey, the preference of 33 architecture-related people on the formal constituents of street buildings was investigated. The results of the document research, field survey and questionnaire survey were put together to elaborate the design guideline for urban street facade in historical city.

• Korean Institute of Interior Design Journal
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• no.27
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• pp.64-75
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• 2001

As living condition has improved, agreeable living environmental plans that reflect residential traits are needed from. the first stage of house remodeling or interior construction. At the request of above, we need systematically study about the householder's preference according to the life style. The purpose of this study is to suggest the case of preferred interior by understanding preferred Interior image and interior design elements. Document and questionnaire research are used as the method of study. The subjects of research wear 702 persons from 20th to 40th. Contents are constituted with general traits, life style and preferred interior image and the analysis of the materials is quantifical analysis using statistics. In the base of the theory of interior image and interior design elements appeared In the documents, measuring apparatus is made up and suggested preferred examples of interior design depend on life style by combining preferences.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.133-136
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• 2000

우리는 인터넷을 통한 사용자의 선호도(preference)를 분석하고 협력적 여과 기술을 학습하여 유머문서를 추천하는 MrHumor 시스템을 구축하였다. MrHumor에서는 사용자집합이 유머문서 집합에 대하여 보여준 등급매김값을 토대로 사용집합의 백터공간(vector space)를 설정하고 노이즈에 강하면서 효율적인 학습을 위해 선형 PCA를 이용하여 축소된 2차원 공간상에서 유머문서의 통계적 특성을 반영하여 적응형 k-NN으로 지엽성을 적적히 조절하여 새로운 문서에 대한 선호도를 추정하게 된다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.238-240
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• 2002

If there is an Information Retrieval system which comprehends the semantic content of documents and knows the preference of users. the system can search the information better on the Internet, or improve the IR performance. Therefore we propose the IR model which combines semantic based indexing and fuzzy relevance model. In addition to the statistical approach, we chose the semantic approach in indexing, lexical chains, because we assume it would improve the performance of the index term extraction. Furthermore, we combined the semantic based indexing with the fuzzy model, which finds out the exact relevance of the user preference and index terms. The proposed system works as follows: First, the presented system indexes documents by the efficient index term extraction method using lexical chains. And then, if a user tends to retrieve the information from the indexed document collection, the extended IR model calculates and ranks the relevance of user query. user preference and index terms by some metrics. When we experimented each module, semantic based indexing and extended fuzzy model. it gave noticeable results. The combination of these modules is expected to improve the information retrieval performance.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.789-796
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• 2005

Because existing information retrieval systems, in particular library retrieval systems, use 'exact keyword matching' with user's query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university' digital library system. In case that many documents are retrieved proposed techniques are able to provide user with filtered data and ranked data according to user's subject and preference.

• Journal of Korean Society of Rural Planning
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• v.16 no.3
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• pp.143-157
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• 2010

This study provides a physical indicator of landscape that can be guidelines so as to evaluate landscape agricultural region for visual landscape planning, finds out the guideline for visual landscape planning lastly through examining importance of indicators and the preference of each indicator of landscape. The physical landscape indicators were derived from document study and questionnaire survey to evaluate visual landscape on landscape agricultural region. In addition, field study was conducted to examine and inspect the physical landscape indicator, managers' interview and photograph was took for evaluating the landscape simulation. Moreover, the important elements for visual landscape planning of landscape agricultural region, the importance of physical landscape indicator and the preferences of each indicator were derived by conducting questionnaire to experts and general publics. The physical landscape indicator guideline was established from the following procedures. In case of the land, flat area had higher preference than steep region. So, planning an agricultural area at a flat region with open space will be better than establishing an agricultural area on a steep region. In case of the kind of landscape crops, For the background of landscape agricultural region, the seashore type had the highest preference and mountain type and non-background type was followed in order. According to the study, facilities built with natural elements such as straw-roofed pavilion received high preference. Therefore, look-out shed and straw-roofed pavilion should be introduced in the landscape agriculture planning to select materials and colors to keep harmony with the nature. The result of this study could be used as a best choice for improving visual landscape of landscape agricultural region on selecting suitable land, facilities and so on. Moreover, the results of manager interview could be used as a useful tool in the management and formation of visual landscape. The landscape point evaluating visual landscape of landscape agricultural region could be used as a reference for establishing relative guideline for the direct payment program for rural landscape conservation and landscape agreement. In addition, it could be a useful reference to improve the general landscape and revitalize the rural area.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.38-46
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• 2009

With the rapid increase in the number of Web documents, the problem of information overload is growing more serious in Internet search. In order to ease the problem, researchers are paying attention to personalization, which creates Web environment fittingly for users' preference, but most of search engines produce results focused on users' queries. Thus, the present study examined the method of producing search results personalized based on a user's understanding level. A characteristic that differentiates this study from previous researches is that it considers users' understanding level and searches documents of difficulty fit for the level first. The difficulty level of a document is adjusted based on the understanding level of users who access the document, and a user's understanding level is updated periodically based on the difficulty of documents accessed by the user. A Web search system based on the results of this study is expected to bring very useful results to Web users of various age groups.

• Journal of the Korean Society of Food Culture
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• v.28 no.5
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• pp.425-437
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• 2013

The aim of this study was to establish the identity of Korean traditional food based on the recorded food preferences during the period of the Chosun Dynasty. Our primary source in this regard was the invaluable, historical document called the "Miam's diary." This important document reveals details of such food preferences from October 1567 to September 1568. By analyzing the income-expenditure trends of virtually every household, this diary was used to describe a vivid traditional food preference of the people during that period. A detailed analysis of the diary reveals the summary of families' characteristics in the 16th century. First, it records the fact that expenditure on food was mainly based on stipend and gifts received. The type of food preferred by the people was diverse in nature; for it included rice, bean, chicken, pheasant, and seafood. However, there were dried or pickled forms too so as to prevent them from undergoing decay. Second, it throws light on the fact that people expended food mainly as a salary for servants. People utilized the income from selling such food items to purchase goods and land. They also used the same either to donate for a funeral or wedding purpose. Third, it records the fact that day-to-day purchase of groceries was mostly based on gift(s) for someone close to them such as a neighbor, colleague, relative, or student. Further, such gifts included small groceries, food items, and clothes. Fourth, based on the data available in the diary, it seemed likely that the gentry families laid emphasis on the customary formalities of a family dating back to as early as the late 16th century. Finally, the document also records the fact that noblemen of the Chosun Dynasty had a notion that they had to extend warmth and affection by presenting generous gifts to their guests at home. Noblemen during that period were very particular in welcoming their guests as they believed that this approach alone would testify their status as noblemen.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.69-74
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• 2018

Through the convergence of Internet technology and various information technologies, it is possible to collect and process vast amount of information and to exchange various knowledge according to user's personal preference. Especially, there is a tendency to prefer intimate contents connected with the user's preference through the flow of emotional changes contained in the movie media. Based on the information presented in the script, the user seeks to visualize the flow of the entire emotion, the flow of emotions in a specific scene, or a specific scene in order to understand it more quickly. In this paper, after obtaining the raw data from the movie web page, it transforms it into a standardized scenario format after refining process. After converting the refined data into an XML document to easily obtain various information, various sentences are predicted by inputting each paragraph into the emotion prediction system. We propose a system that can easily understand the change of the emotional state between the characters in the whole or a specific part of the various emotions required by the user by mixing the predicted emotions flow and the amount of information included in the script.